Estimating landslide susceptibility areas considering the uncertainty inherent in modeling methods-Reference-Cited by-同舟云学术

Estimating landslide susceptibility areas considering the uncertainty inherent in modeling methods

Published:2018-09-27 Issue:11 Volume:32 Page:2987-3019
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Kim Ho Gul,Lee Dong Kun,Park Chan,Ahn Yoonjung,Kil Sung-Ho,Sung Sunyong,Biging Gregory S.

Funder

Korea’s Ministry of Environment

Seoul National University Interdisciplinary Program in Landscape Architecture, Global Leadership Program toward Innovative Green Infrastructure

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science,Safety, Risk, Reliability and Quality,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

http://link.springer.com/article/10.1007/s00477-018-1609-y/fulltext.html

Reference68 articles.

1. Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at Izmir, Turkey. Landslides 9:93–106

2. Akgun A, Dag S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54:1127–1143

3. Althuwaynee OF, Pradhan B, Park H-J, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. CATENA 114:21–36

4. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31

5. Bartlett P, Shawe-Taylor J (1999) Generalization performance of support vector machines and other pattern classifiers. In: Advances in Kernel Methods—Support Vector Learning, pp 43–54

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